Integromic Analysis of Genetic Variation and Gene Expression Identifies Networks for Cardiovascular Disease Phenotypes
Background—Cardiovascular disease (CVD) reflects a highly coordinated complex of traits. Although genome-wide association studies (GWAS) have reported numerous SNPs to be associated with CVD, the role of most of these variants in disease processes remains unknown.
Methods and Results—We built a CVD network using 1,512 SNPs associated with 21 CVD traits in GWAS (at p≤5×10-8) and cross-linked different traits by virtue of their shared SNP associations. We then explored whole blood gene expression in relation to these SNPs in 5,257 participants in the Framingham Heart Study. At a false discovery rate <0.05, we identified 370 cis-eQTLs (SNPs associated with altered expression of nearby genes) and 44 trans-eQTLs (SNPs associated with altered expression of remote genes). The eQTL network revealed 13 CVD-related modules. Searching for association of eQTL genes with CVD risk factors (lipids, blood pressure, fasting blood glucose, and body mass index) in the same individuals, we found examples where the expression of eQTL genes was significantly associated with these CVD phenotypes. In addition, mediation tests suggested that a subset of SNPs previously associated with CVD phenotypes in GWAS, may exert their function by altering expression of eQTL genes (e.g. LDLR, and PCSK7) that in turn may promote inter-individual variation in phenotypes.
Conclusions—Using a network approach to analyze CVD traits, we identified complex networks of SNP-phenotype and SNP-transcript connections. Integrating the CVD network with phenotypic data, we identified biological pathways that may provide insights into potential drug targets for treatment or prevention of CVD.
- Received April 23, 2014.
- Revision received November 24, 2014.
- Accepted December 1, 2014.